A population model for predicting the successful establishment of introduced bird species

Abstract

One of the strongest generalities in invasion biology is the positive relationship between probability of establishment and the numbers of individuals introduced. Nevertheless, a number of significant questions remain regarding: (1) the relative importance of different processes during introduction (e.g., demographic, environmental, and genetic stochasticity, and Allee effects); (2) the relative effects of propagule pressure (e.g., number of introductions, size of introductions, and lag between introductions); and (3) different life history characteristics of the species themselves. Here, we adopt an individual-based simulation modeling approach to explore a range of such details in the relationship between establishment success and numbers of individuals introduced. Our models are developed for typical exotic bird introductions, for which the relationship between probability of establishment and the numbers of individuals introduced has been particularly well documented. For both short-lived and long-lived species, probability of establishment decreased across multiple introductions (compared with a single introduction of the same total size), and this decrease was greater when inbreeding depression was included. Sensitivity analyses revealed four predictors that together accounted for >95 % of model performance. Of these, R0 (the average number of daughters produced per female over her lifetime) and propagule pressure were of primary importance, while random environmental effects and inbreeding depression exerted lesser influence. Initial founder size is undoubtedly going to be important for ensuring the persistence of introduced populations. However, we found the demographic traits, which influence how introduced individuals behave, to have the greatest effect on establishment success.

Keywords

PC, TAAP, and TMB formulated the idea for the study and designed the analyses. TAAP conducted the simulations, and TAAP and PC conducted the statistical analyses. PC, TAAP, and TMB wrote the manuscript.

Electronic supplementary material

The online version of this article (doi:10.1007/s00442-014-2902-1) contains supplementary material, which is available to authorized users.

Notes

Acknowledgments

PC, TAAP, and TMB formulated the idea for the study and designed the analyses. TAAP conducted the simulations, and TAAP and PC conducted the statistical analyses. PC, TAAP, and TMB wrote the manuscript. Communicated by Ola Olsson. We are grateful to BW Brook for insightful discussions that greatly improved this manuscript. Comments from Ola Olsson and two anonymous referees were gratefully received. PC is an ARC Future Fellow (FT0991420).

Supplementary material

Supplementary material 1 Fig. S1 Partial dependency plots (on the logit-scale) derived from boosted regression tree (BRT) analysis of the sensitivity analysis output for single introduction scenarios only (see the selected model in Table 3 of the main text). The BRT model was fitted with simulation outcome (establishment success or failure) as the response and used ten simulation parameters as predictors of the outcome: net reproductive rate (R0, calculated from the survival and fertility rates specified) and all other non-demographic simulation parameters that were varied in the sensitivity analysis. Each of these individual partial dependency plots assumes unplotted parameters are set at their mean values. The relative influence of each predictor in the fitted BRT model is shown in brackets on the x-axis. (JPEG 3934 kb)

Supplementary material 2 Fig. S2 Partial dependency plots (on the logit-scale) derived from boosted regression tree (BRT) analysis of the sensitivity analysis output for multiple introduction scenarios only (see the selected model in Table 3 of the main text). The BRT model was fitted with simulation outcome (establishment success or failure) as the response and used twelve simulation parameters as predictors of the outcome: net reproductive rate (R0, calculated from the survival and fertility rates specified) and all other non-demographic simulation parameters that were varied in the sensitivity analysis. Each of these individual partial dependency plots assumes unplotted parameters are set at their mean values. The relative influence of each predictor in the fitted BRT model is shown in brackets on the x-axis. (JPEG 4365 kb)

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